Safety instrumented systems must be designed, built and operated to meet tolerable risk level as required regulatory agencies. This requirement is closely related to their probabilistic performance measures which are either their average probability of dangerous failure on demand (PFDavg) or their average frequency of failure (PFH: Probability of Failure per Hour). The object of this work is the SIS performances evaluation taking into account the uncertainties related to the different parameters that come into play: failure rate (λ), common cause failure proportion (β), diagnostic coverage (DC), etc. This leads to an accurate and therefore safe assessment of the safety integrity level (SIL) inherent to safety functions performed by such systems. This aim is in keeping with the requirement of the IEC 61508 standard with respect to handling uncertainty. In this paper we first explain in detail the IEC 61508 approach for handling uncertainty. Afterwards, we propose an approach that combines (i) Monte Carlo analysis (MCA) and (ii) fuzzy sets. Indeed, the first method is appropriate when representative statistical data are available (using pdf of the relating parameters), while the latter applies in the case characterized by vague and subjective information (using membership function). The proposed approach is fully supported with a suitable computer code developed under the MATLAB environment.

We consider a system of transmission of the wave equation with Neumann feedback control that contains a delay term and acts on the exterior boundary. First, we prove under some assumptions that the closed-loop system generates a C 0 -semigroup of contractions on an appropriate Hilbert space. Then, under further assumptions, we show that the closed-loop system is exponentially stable. To establish this result, we introduce a suitable energy function and use multiplier method together with an estimate taken from Lasiecka & Triggiani (1992, Appl. Math. Optim., 25, 189–244.) (Lemma 7.2) and compactness-uniqueness argument.

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90% for 2MHz and to 99.60% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.

In this study, pomace olives coming from different mills (Press process, continuous process two-phases and threephases) were used for extraction of oils. The highest oil yield (12.92%) was obtained with pomace olives coming from press process. Total contents of phenolics (13.47 - 21.25 mg GAE/100 g oil) and flavonoids (5.90 - 12.52mg QE/100 g oil)were determined spectrophotometrically. The pomace olive oil “POO3” (Pomace olive coming from 3-phases system) presented the highest phenolic, flavonoid contents and showed the highest DPPH, ABTS scavenging, metal chelating activity. In vitro anticholinesterase activity, the olive pomace oils showed moderate inhibition against AChE and BChE which are the key enzymes taking place in pathogenesis of Alzheimer’s disease. These results showed that the tested oils can be considered as sources of natural antioxidant, as well as moderate anticholinesterase agents.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

Phase diagrams are representations of the sate of a material as a function of temperature of thee constituent components. Multi-components phase diagrams are useful for alloy development processing and understanding. They are considered as roadmaps in design of new materials used in technology. To establish phase diagrams we usually use a thermodynamic approach called CALPHAD (CALculation PHase Diagrams) based on the knowledge and minimization of the Gibbs energy of all phases taking part in the system. We can obtain the Gibbs energy of each phase by using experimental methods (calorimetry). We can also use theoretical method (ab-initio) or a semiempirical model of Miedema. We reviewed and used three calorimetric methods: direct reaction, progressive precipitation and indirect mixing calorimetry. We obtained at high temperatures the enthalpies of formations of eight compounds: Ga3Mo, Ga5Mo, GaNb3, Ga5Nb4, Ga3Nb,  -GaCr3,  -GaCr3 and Ga4Cr. Their values were compared to those estimated theoretically, reviewed and commented. In addition to their exploitation to establish phase diagrams, these data permits to improve thermodynamic models and understand the behaviours of gallium towards the three transition metals molybdenum, niobium and chromium. This procedure can be applied do ternary compound if needed.